Journal Article

DKFZ-2021-01577

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Investigating particle track topology for range telescopes in particle radiography using convolutional neural networks.

Pettersen, H. E. S. ; Aehle, M. ; Alme, J. ; Barnaföldi, G. G. ; Borshchov, V. ; van den Brink, A. ; Chaar, M. ; Eikeland, V. ; Feofilov, G. ; Garth, C. ; Gauger, N. R. ; Genov, G. ; Grøttvik, O. ; Helstrup, H. ; Igolkin, S. ; Keidel, R. ; Kobdaj, C. ; Kortus, T. ; Leonhardt, V. ; Mehendale, S. ; Mulawade, R. N. ; Odland, O. H. ; Papp, G. ; Peitzmann, T. ; Piersimoni, P. ; Protsenko, M. ; Rehman, A. U. ; Richter, M. ; Santana, J. ; Schilling, A. ; Seco, J.DKFZ* ; Songmoolnak, A. ; Sølie, J. R. ; Tambave, G. ; Tymchuk, I. ; Ullaland, K. ; Varga-Kofarago, M. ; Volz, L. ; Wagner, B. ; Wendzel, S. ; Wiebel, A. ; Xiao, R. ; Yang, S. ; Yokoyama, H. ; Zillien, S. ; Röhrich, D.

2021
Taylor & Francis Group Abingdon

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Please use a persistent id in citations: doi:10.1080/0284186X.2021.1949037

Abstract: Proton computed tomography (pCT) and radiography (pRad) are proposed modalities for improved treatment plan accuracy and in situ treatment validation in proton therapy. The pCT system of the Bergen pCT collaboration is able to handle very high particle intensities by means of track reconstruction. However, incorrectly reconstructed and secondary tracks degrade the image quality. We have investigated whether a convolutional neural network (CNN)-based filter is able to improve the image quality.The CNN was trained by simulation and reconstruction of tens of millions of proton and helium tracks. The CNN filter was then compared to simple energy loss threshold methods using the Area Under the Receiver Operating Characteristics curve (AUROC), and by comparing the image quality and Water Equivalent Path Length (WEPL) error of proton and helium radiographs filtered with the same methods.The CNN method led to a considerable improvement of the AUROC, from 74.3% to 97.5% with protons and from 94.2% to 99.5% with helium. The CNN filtering reduced the WEPL error in the helium radiograph from 1.03 mm to 0.93 mm while no improvement was seen in the CNN filtered pRads.The CNN improved the filtering of proton and helium tracks. Only in the helium radiograph did this lead to improved image quality.

Keyword(s): Monte Carlo simulation ; Proton computed tomography ; convolutional neural network ; machine learning ; secondary particles ; track reconstruction

Note: 2021 Nov;60(11):1413-1418

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Contributing Institute(s):

1. E041 Medizinische Physik in der Radioonkologie (E041)

Research Program(s):

1. 315 - Bildgebung und Radioonkologie (POF4-315) (POF4-315)

Appears in the scientific report 2021

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Database coverage:
; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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